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Technical Support feedbacks

Frequency table

Stars	Frequency	Percentage
5	246	96%
4	8	3.1%
3	1	0.4%
2	1	0.4%
1	0	0%

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Ape Configuration XML Files


Ape configures mappers using configuration files.  The standard Zope 2
mapper configuration is in the file 'apeconf.xml' in the
'apelib.zope2' package.  Refer to the standard configuration file as
an example.

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Ape Configuration XML Files


Ape configures mappers using configuration files.  The standard Zope 2
mapper configuration is in the file 'apeconf.xml' in the
'apelib.zope2' package.  Refer to the standard configuration file as
an example.

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@Test
    public void sendUdp() {

        Netty netty = new Netty();
        Channel channel = netty.bootstrap();
        try {
            ChannelFuture channelFuture = channel.writeAndFlush(new Object());
            channelFuture.addListener(new ChannelFutureListener() {
                public void operationComplete(ChannelFuture future) {
                    LOG.info("done!!");

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Technical Challenge

Prompt #1

Trello: https://trello.com/b/29ZHBc2k/tse-bug-board-cristian #TODO: assign permitions to all 3. Loom video: ``

Prompt #2

Prompt #3

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burro side:

local_net="192.168.2.0/26"
remote_net="192.168.4.0/30"
local_gw="186.67.181.205"
peer_gw="178.22.66.173"
state="passive"
ikev2 "vpn-zrh" $state esp \
  from $local_net to $remote_net \
  local $local_gw peer $peer_gw \

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MAX_INT = 2 ** 31 - 1
ans = 0


def merge(a, p, q, r):
    left = list(a[p:q])
    right = list(a[q:r])

    for i in xrange(len(left)):
        for j in xrange(len(right)):

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# Project 1: Model Evaluation & Validation
## Predicting Boston Housing Prices

### Install

This project requires **Python 2.7** and the following Python libraries installed:

- [NumPy](http://www.numpy.org/)
- [matplotlib](http://matplotlib.org/)
- [scikit-learn](http://scikit-learn.org/stable/)

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# In this exercise we'll examine a learner which has high bias, and is incapable of
# learning the patterns in the data.
# Use the learning curve function from sklearn.learning_curve to plot learning curves
# of both training and testing error. Use plt.plot() within the plot_curve function
# to create line graphs of the values.

from sklearn.linear_model import LinearRegression
from sklearn.learning_curve import learning_curve
import matplotlib.pyplot as plt
from sklearn.metrics import explained_variance_score, make_scorer

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import peak
import trace

################################################################################
################################## Algorithms ##################################
################################################################################

def algorithm1(problem, trace = None):
    # if it's empty, we're done 
    if problem.numRow <= 0 or problem.numCol <= 0: